Paper Type
ERF
Abstract
The pharmaceutical industry is expected to experience a large-scale loss of exclusivity, with up to 64% of blockbuster drug revenues projected to be at risk by 2030. In response, Big Pharma is increasingly relying on external innovation, such as mergers and acquisitions (M&A) and strategic alliances. Meanwhile, artificial intelligence (AI) is reshaping drug discovery, yet its systemic impact on long-term firm performance remains underexplored. This study employs System Dynamics (SD) modeling to examine how external innovation dependence, moderated by AI capability, shapes pharmaceutical firms' pipeline structures and long-term financial performance. We conceptualize AI as an organizational capability that accumulates over time through both internal development and external sourcing, and measure it through observable stock-and-flow channels, including AI patents, AI-involved business development (BD) deals, and AI deployment in clinical trials. The model is built around feedback loops capturing internal research and development (R&D), patent cliff erosion, AI-driven acceleration, and external sourcing, providing an exploratory framework for analyzing how these forces interact over time.
Paper Number
1237
Recommended Citation
Yao, Yao; Wang, Wenru; Hassan, Mahmud Ph.D; and Vasarhelyi, Miklos A., "Will AI Reshape Pharmaceutical Business Development Strategy? Evidence from Big Pharma's Responses to the Patent Cliff" (2026). AMCIS 2026 Proceedings. 3.
https://aisel.aisnet.org/amcis2026/sig_osra/sig_osra/3
Will AI Reshape Pharmaceutical Business Development Strategy? Evidence from Big Pharma's Responses to the Patent Cliff
The pharmaceutical industry is expected to experience a large-scale loss of exclusivity, with up to 64% of blockbuster drug revenues projected to be at risk by 2030. In response, Big Pharma is increasingly relying on external innovation, such as mergers and acquisitions (M&A) and strategic alliances. Meanwhile, artificial intelligence (AI) is reshaping drug discovery, yet its systemic impact on long-term firm performance remains underexplored. This study employs System Dynamics (SD) modeling to examine how external innovation dependence, moderated by AI capability, shapes pharmaceutical firms' pipeline structures and long-term financial performance. We conceptualize AI as an organizational capability that accumulates over time through both internal development and external sourcing, and measure it through observable stock-and-flow channels, including AI patents, AI-involved business development (BD) deals, and AI deployment in clinical trials. The model is built around feedback loops capturing internal research and development (R&D), patent cliff erosion, AI-driven acceleration, and external sourcing, providing an exploratory framework for analyzing how these forces interact over time.
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